Owens

TIP: Remember to check the menu to the right of the page for related pages with more information about Owens' specifics.

OSC's Owens cluster being installed in 2016 is a Dell-built, Intel® Xeon® processor-based supercomputer.

Owens infographic,

Hardware

Owens_image

Detailed system specifications:

The memory information below is for total memory (RAM) on the node, however some amount of that is reserved for system use and not available to user processes.
Please see owens batch limits for details on user available memory amount.
  • 824 Dell Nodes
  • Dense Compute
    • 648 compute nodes (Dell PowerEdge C6320 two-socket servers with Intel Xeon E5-2680 v4 (Broadwell, 14 cores, 2.40GHz) processors, 128GB memory)

  • GPU Compute

    • 160 ‘GPU ready’ compute nodes -- Dell PowerEdge R730 two-socket servers with Intel Xeon E5-2680 v4 (Broadwell, 14 cores, 2.40GHz) processors, 128GB memory

    • NVIDIA Tesla P100 (Pascal) GPUs -- 5.3TF peak (double precision), 16GB memory

  • Analytics

    • 16 huge memory nodes (Dell PowerEdge R930 four-socket server with Intel Xeon E5-4830 v3 (Haswell 12 core, 2.10GHz) processors, 1,536GB memory, 12 x 2TB drives)

  • 23,392 total cores
    • 28 cores/node  & 128GB of memory/node
  • Mellanox EDR (100Gbps) Infiniband networking
  • Theoretical system peak performance
    • ~750 teraflops (CPU only)
  • 4 login nodes:
    • Intel Xeon E5-2680 (Broadwell) CPUs
    • 28 cores/node and 256GB of memory/node

How to Connect

  • SSH Method

To login to Owens at OSC, ssh to the following hostname:

owens.osc.edu 

You can either use an ssh client application or execute ssh on the command line in a terminal window as follows:

ssh <username>@owens.osc.edu

You may see warning message including SSH key fingerprint. Verify that the fingerprint in the message matches one of the SSH key fingerprint listed here, then type yes.

From there, you are connected to Owens login node and have access to the compilers and other software development tools. You can run programs interactively or through batch requests. We use control groups on login nodes to keep the login nodes stable. Please use batch jobs for any compute-intensive or memory-intensive work. See the following sections for details.

  • OnDemand Method

You can also login to Owens at OSC with our OnDemand tool. The first step is to login to OnDemand. Then once logged in you can access Owens by clicking on "Clusters", and then selecting ">_Owens Shell Access".

Instructions on how to connect to OnDemand can be found at the OnDemand documention page.

File Systems

Owens accesses the same OSC mass storage environment as our other clusters. Therefore, users have the same home directory as on the old clusters. Full details of the storage environment are available in our storage environment guide.

Software Environment

The module system is used to manage the software environment on owens. Use  module load <package>  to add a software package to your environment. Use  module list  to see what modules are currently loaded and  module avail  to see the modules that are available to load. To search for modules that may not be visible due to dependencies or conflicts, use  module spider . By default, you will have the batch scheduling software modules, the Intel compiler and an appropriate version of mvapich2 loaded.

You can keep up to on the software packages that have been made available on Owens by viewing the Software by System page and selecting the Owens system.

Compiling Code to Use Advanced Vector Extensions (AVX2)

The Haswell and Broadwell processors that make up Owens support the Advanced Vector Extensions (AVX2) instruction set, but you must set the correct compiler flags to take advantage of it. AVX2 has the potential to speed up your code by a factor of 4 or more, depending on the compiler and options you would otherwise use.

In our experience, the Intel and PGI compilers do a much better job than the gnu compilers at optimizing HPC code.

With the Intel compilers, use -xHost and -O2 or higher. With the gnu compilers, use -march=native and -O3 . The PGI compilers by default use the highest available instruction set, so no additional flags are necessary.

This advice assumes that you are building and running your code on Owens. The executables will not be portable.  Of course, any highly optimized builds, such as those employing the options above, should be thoroughly validated for correctness.

See the Owens Programming Environment page for details.

Batch Specifics

Refer to the documentation for our batch environment to understand how to use the batch system on OSC hardware. Some specifics you will need to know to create well-formed batch scripts:

  • Most compute nodes on Owens have 28 cores/processors per node.  Huge-memory (analytics) nodes have 48 cores/processors per node.
  • Jobs on Owens may request partial nodes.

Using OSC Resources

For more information about how to use OSC resources, please see our guide on batch processing at OSC. For specific information about modules and file storage, please see the Batch Execution Environment page.

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Technical Specifications

The following are technical specifications for Owens.  

  Owens SYSTEM (2016)
NUMBER OF NODES 824 nodes
NUMBER OF CPU SOCKETS 1648 (2 sockets/node)
NUMBER OF CPU CORES 23,392 (28 cores/node)
CORES PER NODE 28 cores/node (48 cores/node for Huge Mem Nodes)
LOCAL DISK SPACE PER NODE

~1500GB in /tmp

COMPUTE CPU SPECIFICATIONS

Intel Xeon E5-2680 v4 (Broadwell) for compute

  • 2.4 GHz 
  • 14 cores per processor
COMPUTER SERVER SPECIFICATIONS

648 Dell PowerEdge C6320

160 Dell PowerEdge R730 (for accelerator nodes)

ACCELERATOR SPECIFICATIONS

NVIDIA P100 "Pascal" GPUs 16GB memory

NUMBER OF ACCELERATOR NODES

160 total

TOTAL MEMORY ~ 127 TB
MEMORY PER NODE

128 GB (1.5 TB for Huge Mem Nodes)

MEMORY PER CORE 4.5 GB (31 GB for Huge Mem)
INTERCONNECT  Mellanox EDR Infiniband Networking (100Gbps)
LOGIN SPECIFICATIONS

4 Intel Xeon E5-2680 (Broadwell) CPUs

  • 28 cores/node and 256GB of memory/node
SPECIAL NODES

16 Huge Memory Nodes

  • Dell PowerEdge R930 
  • 4 Intel Xeon E5-4830 v3 (Haswell)
    • 12 Cores
    • 2.1 GHz
  • 48 cores (12 cores/CPU)
  • 1.5 TB Memory
  • 12 x 2 TB Drive (20TB usable)

 

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Environment changes in Slurm migration

As we migrate to Slurm from Torque/Moab, there will be necessary software environment changes.

Decommissioning old MVAPICH2 versions

Old MVAPICH2 including mvapich2/2.1mvapich2/2.2 and its variants do not support Slurm very well due to its life span, so we will remove the following versions:

  • mvapich2/2.1
  • mvapich2/2.2, 2.2rc1, 2.2ddn1.3, 2.2ddn1.4, 2.2-debug, 2.2-gpu

As a result, the following dependent software will not be available anymore.

Unavailable Software Possible replacement
amber/16 amber/18
darshan/3.1.4 darshan/3.1.6
darshan/3.1.5-pre1 darshan/3.1.6
expresso/5.2.1 expresso/6.3
expresso/6.1 expresso/6.3
expresso/6.1.2 expresso/6.3
fftw3/3.3.4 fftw3/3.3.5
gamess/18Aug2016R1 gamess/30Sep2019R2
gromacs/2016.4 gromacs/2018.2
gromacs/5.1.2 gromacs/2018.2
lammps/14May16 lammps/16Mar18
lammps/31Mar17 lammps/16Mar18
mumps/5.0.2 N/A (no current users)
namd/2.11 namd/2.13
nwchem/6.6 nwchem/6.8
pnetcdf/1.7.0 pnetcdf/1.10.0
siesta-par/4.0 siesta-par/4.0.2

If you used one of the software listed above, we strongly recommend testing during the early user period. We listed a possible replacement version that is close to the unavailable version. However, if it is possible, we recommend using the most recent versions available. You can find the available versions by module spider {software name}. If you have any questions, please contact OSC Help.

Miscellaneous cleanup on MPIs

We clean up miscellaneous MPIs as we have a better and compatible version available. Since it has a compatible version, you should be able to use your applications without issues.

Removed MPI versions Compatible MPI versions

mvapich2/2.3b

mvapich2/2.3rc1

mvapich2/2.3rc2

mvapich2/2.3

mvapich2/2.3.3

mvapich2/2.3b-gpu

mvapich2/2.3rc1-gpu

mvapich2/2.3rc2-gpu

mvapich2/2.3-gpu

mvapich2/2.3.1-gpu

mvapich2-gdr/2.3.1, 2.3.2, 2.3.3

mvapich2-gdr/2.3.4

openmpi/1.10.5

openmpi/1.10

openmpi/1.10.7

openmpi/1.10.7-hpcx

openmpi/2.0

openmpi/2.0.3

openmpi/2.1.2

openmpi/2.1.6

openmpi/2.1.6-hpcx

openmpi/4.0.2

openmpi/4.0.2-hpcx

openmpi/4.0.3

openmpi/4.0.3-hpcx

Software flag usage update for Licensed Software

We have software flags required to use in job scripts for licensed software, such as ansys, abauqs, or schrodinger. With the slurm migration, we updated the syntax and added extra software flags.  It is very important everyone follow the procedure below. If you don't use the software flags properly, jobs submitted by others can be affected. 

We require using software flags only for the demanding software and the software features in order to prevent job failures due to insufficient licenses. When you use the software flags, Slurm will record it on its license pool, so that other jobs will launch when there are enough licenses available. This will function correctly when everyone uses the software flag.

During the early user period until Dec 15, 2020, the software flag system may not work correctly. This is because, during the test period, licenses will be used from two separate Owens systems. However, we recommend you to test your job scripts with the new software flags, so that you can use it without any issues after the slurm migration.

The new syntax for software flags is

#SBATCH -L {software flag}@osc:N

where N is the requesting number of the licenses. If you need more than one software flags, you can use

#SBATCH -L {software flag1}@osc:N,{software flag2}@osc:M

For example, if you need 2 abaqus and 2 abaqusextended license features, then you can use

$SBATCH -L abaqus@osc:2,abaqusextended@osc:2

We have the full list of software associated with software flags in the table below.

  Software flag Note
abaqus

abaqus, abaquscae

 
ansys ansys, ansyspar  
comsol comsolscript  
schrodinger epik, glide, ligprep, macromodel, qikprop  
starccm starccm, starccmpar  
stata stata  
usearch usearch  
ls-dyna, mpp-dyna lsdyna  
 
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Owens Programming Environment (PBS)

This document is obsoleted and kept as a reference to previous Owens programming environment. Please refer to here for the latest version.

Compilers

C, C++ and Fortran are supported on the Owens cluster. Intel, PGI and GNU compiler suites are available. The Intel development tool chain is loaded by default. Compiler commands and recommended options for serial programs are listed in the table below. See also our compilation guide.

The Haswell and Broadwell processors that make up Owens support the Advanced Vector Extensions (AVX2) instruction set, but you must set the correct compiler flags to take advantage of it. AVX2 has the potential to speed up your code by a factor of 4 or more, depending on the compiler and options you would otherwise use.

In our experience, the Intel and PGI compilers do a much better job than the GNU compilers at optimizing HPC code.

With the Intel compilers, use -xHost and -O2 or higher. With the GNU compilers, use -march=native and -O3. The PGI compilers by default use the highest available instruction set, so no additional flags are necessary.

This advice assumes that you are building and running your code on Owens. The executables will not be portable.  Of course, any highly optimized builds, such as those employing the options above, should be thoroughly validated for correctness.

LANGUAGE INTEL EXAMPLE PGI EXAMPLE GNU EXAMPLE
C icc -O2 -xHost hello.c pgcc -fast hello.c gcc -O3 -march=native hello.c
Fortran 90 ifort -O2 -xHost hello.f90 pgf90 -fast hello.f90 gfortran -O3 -march=native hello.f90
C++ icpc -O2 -xHost hello.cpp pgc++ -fast hello.cpp g++ -O3 -march=native hello.cpp

Parallel Programming

MPI

OSC systems use the MVAPICH2 implementation of the Message Passing Interface (MPI), optimized for the high-speed Infiniband interconnect. MPI is a standard library for performing parallel processing using a distributed-memory model. For more information on building your MPI codes, please visit the MPI Library documentation.

Parallel programs are started with the mpiexec command. For example,

mpiexec ./myprog

The program to be run must either be in your path or have its path specified.

The mpiexec command will normally spawn one MPI process per CPU core requested in a batch job. Use the -n and/or -ppn option to change that behavior.

The table below shows some commonly used options. Use mpiexec -help for more information.

MPIEXEC Option COMMENT
-ppn 1 One process per node
-ppn procs procs processes per node
-n totalprocs
-np totalprocs
At most totalprocs processes per node
-prepend-rank Prepend rank to output
-help Get a list of available options

 

Caution: There are many variations on mpiexec and mpiexec.hydra. Information found on non-OSC websites may not be applicable to our installation.
The information above applies to the MVAPICH2 and IntelMPI installations at OSC. See the OpenMPI software page for mpiexec usage with OpenMPI.

OpenMP

The Intel, PGI and GNU compilers understand the OpenMP set of directives, which support multithreaded programming. For more information on building OpenMP codes on OSC systems, please visit the OpenMP documentation.

GPU Programming

160 Nvidia P100 GPUs are available on Owens.  Please visit our GPU documentation.

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Owens Programming Environment

Compilers

C, C++ and Fortran are supported on the Owens cluster. Intel, PGI and GNU compiler suites are available. The Intel development tool chain is loaded by default. Compiler commands and recommended options for serial programs are listed in the table below. See also our compilation guide.

The Haswell and Broadwell processors that make up Owens support the Advanced Vector Extensions (AVX2) instruction set, but you must set the correct compiler flags to take advantage of it. AVX2 has the potential to speed up your code by a factor of 4 or more, depending on the compiler and options you would otherwise use.

In our experience, the Intel and PGI compilers do a much better job than the GNU compilers at optimizing HPC code.

With the Intel compilers, use -xHost and -O2 or higher. With the GNU compilers, use -march=native and -O3. The PGI compilers by default use the highest available instruction set, so no additional flags are necessary.

This advice assumes that you are building and running your code on Owens. The executables will not be portable.  Of course, any highly optimized builds, such as those employing the options above, should be thoroughly validated for correctness.

LANGUAGE INTEL GNU PGI
C icc -O2 -xHost hello.c gcc -O3 -march=native hello.c pgcc -fast hello.c
Fortran 77/90 ifort -O2 -xHost hello.F gfortran -O3 -march=native hello.F pgfortran -fast hello.F
C++ icpc -O2 -xHost hello.cpp g++ -O3 -march=native hello.cpp pgc++ -fast hello.cpp

Parallel Programming

MPI

OSC systems use the MVAPICH2 implementation of the Message Passing Interface (MPI), optimized for the high-speed Infiniband interconnect. MPI is a standard library for performing parallel processing using a distributed-memory model. For more information on building your MPI codes, please visit the MPI Library documentation.

MPI programs are started with the srun command. For example,

#!/bin/bash
#SBATCH --nodes=2

srun [ options ] mpi_prog
Note: the program to be run must either be in your path or have its path specified.

The srun command will normally spawn one MPI process per task requested in a Slurm batch job. Use the -n ntasks and/or --ntasks-per-node=n option to change that behavior. For example,

#!/bin/bash
#SBATCH --nodes=2

# Use the maximum number of CPUs of two nodes
srun ./mpi_prog

# Run 8 processes per node
srun -n 16 --ntasks-per-node=8  ./mpi_prog

The table below shows some commonly used options. Use srun -help for more information.

OPTION COMMENT
-n, --ntasks=ntasks total number of tasks to run
--ntasks-per-node=n number of tasks to invoke on each node
-help Get a list of available options
Note: The information above applies to the MVAPICH2, Intel MPI and OpenMPI installations at OSC. 
Caution: mpiexec or mpiexec.hydra is still supported with Intel MPI and OpenMPI, but it is not fully compatible in our Slurm environment. We recommand using srun in any circumstances.

OpenMP

The Intel, GNU and PGI compilers understand the OpenMP set of directives, which support multithreaded programming. For more information on building OpenMP codes on OSC systems, please visit the OpenMP documentation.

An OpenMP program by default will use a number of threads equal to the number of CPUs requested in a Slurm batch job. To use a different number of threads, set the environment variable OMP_NUM_THREADS. For example,

#!/bin/bash
#SBATCH --ntasks=8

# Run 8 threads
./omp_prog

# Run 4 threads
export OMP_NUM_THREADS=4
./omp_prog

To run a OpenMP job on an exclusive node:

#!/bin/bash
#SBATCH --nodes=1
#SBATCH --exclusive

export OMP_NUM_THREADS=$SLURM_CPUS_ON_NODE
./omp_prog

Interactive job only

See the section on interactive batch in batch job submission for details on submitting an interactive job to the cluster.

Hybrid (MPI + OpenMP)

An example of running a job for hybrid code:

#!/bin/bash
#SBATCH --nodes=2

# Run 4 MPI processes on each node and 7 OpenMP threads spawned from a MPI process
export OMP_NUM_THREADS=7
srun -n 8 -c 7 --ntasks-per-node=4 ./hybrid_prog

 

Tuning Parallel Program Performance: Process/Thread Placement

To get the maximum performance, it is important to make sure that processes/threads are located as close as possible to their data, and as close as possible to each other if they need to work on the same piece of data, with given the arrangement of node, sockets, and cores, with different access to RAM and caches. 

While cache and memory contention between threads/processes are an issue, it is best to use scatter distribution for code. 

Processes and threads are placed differently depending on the computing resources you requste and the compiler and MPI implementation used to compile your code. For the former, see the above examples to learn how to run a job on exclusive nodes. For the latter, this section summarizes the default behavior and how to modify placement.

OpenMP only

For all three compilers (Intel, GNU, PGI), purely threaded codes do not bind to particular CPU cores by default. In other words, it is possible that multiple threads are bound to the same CPU core

The following table describes how to modify the default placements for pure threaded code:

DISTRIBUTION Compact Scatter/Cyclic
DESCRIPTION Place threads as closely as possible on sockets Distribute threads as evenly as possible across sockets
INTEL KMP_AFFINITY=compact KMP_AFFINITY=scatter
GNU OMP_PLACES=sockets[1] OMP_PROC_BIND=spread/close
PGI[2]

MP_BIND=yes
MP_BLIST="$(seq -s, 0 2 27),$(seq -s, 1 2 27)" 

MP_BIND=yes
  1. Threads in the same socket might be bound to the same CPU core.
  2. PGI LLVM-backend (version 19.1 and later) does not support thread/processors affinity on NUMA architecture. To enable this feature, compile threaded code with --Mnollvm to use proprietary backend.

MPI Only

For MPI-only codes, MVAPICH2 first binds as many processes as possible on one socket, then allocates the remaining processes on the second socket so that consecutive tasks are near each other.  Intel MPI and OpenMPI alternately bind processes on socket 1, socket 2, socket 1, socket 2 etc, as cyclic distribution.

For process distribution across nodes, all MPIs first bind as many processes as possible on one node, then allocates the remaining processes on the second node. 

The following table describe how to modify the default placements on a single node for MPI-only code with the command srun:

DISTRIBUTION
(single node)
Compact Scatter/Cyclic
DESCRIPTION Place processs as closely as possible on sockets Distribute process as evenly as possible across sockets
MVAPICH2[1] Default MV2_CPU_BINDING_POLICY=scatter
INTEL MPI srun --cpu-bind="map_cpu:$(seq -s, 0 2 27),$(seq -s, 1 2 27)" Default
OPENMPI srun --cpu-bind="map_cpu:$(seq -s, 0 2 27),$(seq -s, 1 2 27)" Default
  1. MV2_CPU_BINDING_POLICY will not work if MV2_ENABLE_AFFINITY=0 is set.

To distribute processes evenly across nodes, please set SLURM_DISTRIBUTION=cyclic.

Hybrid (MPI + OpenMP)

For Hybrid codes, each MPI process is allocated  OMP_NUM_THREADS cores and the threads of each process are bound to those cores. All MPI processes (as well as the threads bound to the process) behave as we describe in the previous sections. It means the threads spawned from a MPI process might be bound to the same core. To change the default process/thread placmements, please refer to the tables above. 

Summary

The above tables list the most commonly used settings for process/thread placement. Some compilers and Intel libraries may have additional options for process and thread placement beyond those mentioned on this page. For more information on a specific compiler/library, check the more detailed documentation for that library.

GPU Programming

160 Nvidia P100 GPUs are available on Owens.  Please visit our GPU documentation.

Reference

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Citation

For more information about citations of OSC, visit https://www.osc.edu/citation.

To cite Owens, please use the following Archival Resource Key:

ark:/19495/hpc6h5b1

Please adjust this citation to fit the citation style guidelines required.

Ohio Supercomputer Center. 2016. Owens Supercomputer. Columbus, OH: Ohio Supercomputer Center. http://osc.edu/ark:19495/hpc6h5b1

Here is the citation in BibTeX format:

@misc{Owens2016,
ark = {ark:/19495/hpc93fc8},
url = {http://osc.edu/ark:/19495/hpc6h5b1},
year  = {2016},
author = {Ohio Supercomputer Center},
title = {Owens Supercomputer}
}

And in EndNote format:

%0 Generic
%T Owens Supercomputer
%A Ohio Supercomputer Center
%R ark:/19495/hpc6h5b1
%U http://osc.edu/ark:/19495/hpc6h5b1
%D 2016

Here is an .ris file to better suit your needs. Please change the import option to .ris.

Documentation Attachment: 
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Owens SSH key fingerprints

These are the public key fingerprints for Owens:
owens: ssh_host_rsa_key.pub = 18:68:d4:b0:44:a8:e2:74:59:cc:c8:e3:3a:fa:a5:3f
owens: ssh_host_ed25519_key.pub = 1c:3d:f9:99:79:06:ac:6e:3a:4b:26:81:69:1a:ce:83
owens: ssh_host_ecdsa_key.pub = d6:92:d1:b0:eb:bc:18:86:0c:df:c5:48:29:71:24:af


These are the SHA256 hashes:​
owens: ssh_host_rsa_key.pub = SHA256:vYIOstM2e8xp7WDy5Dua1pt/FxmMJEsHtubqEowOaxo
owens: ssh_host_ed25519_key.pub = SHA256:FSb9ZxUoj5biXhAX85tcJ/+OmTnyFenaSy5ynkRIgV8
owens: ssh_host_ecdsa_key.pub = SHA256:+fqAIqaMW/DUJDB0v/FTxMT9rkbvi/qVdMKVROHmAP4

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Batch Limit Rules

Memory Limit:

It is strongly suggested to consider the available per-core memory when users request OSC resources for their jobs.

Summary

Node type default memory per core (gb) max usable memory (gb)
regular compute 4.214GB 117GB
huge memory 31.104GB 1492GB
gpu 4.214GB 117GB

It is recommended to let the default memory apply unless more control over memory is needed.
Note that if an entire node is requested, then the job is automatically granted the entire node's main memory. On the other hand, if a partial node is requested, then memory is granted based on the default memory per core.

See a more detailed explanation below.

Regular Dense Compute Node

On Owens, it equates to 4,315MB/core or 120,820 MB/node (117.98GB/node) for the regular dense compute node. 

If your job requests less than a full node ( ntasks< 28 ), it may be scheduled on a node with other running jobs. In this case, your job is entitled to a memory allocation proportional to the number of cores requested (4315MB/core).  For example, without any memory request ( mem=XXMB ), a job that requests  --nodes=1 --ntasks=1 will be assigned one core and should use no more than 4315MB of RAM, a job that requests  --nodes=1 --ntasks=3  will be assigned 3 cores and should use no more than 3*4315MB of RAM, and a job that requests --nodes=1 --ntasks=28 will be assigned the whole node (28 cores) with 118GB of RAM.  

Here is some information when you include memory request (mem=XX ) in your job. A job that requests  --nodes=1 --ntasks=1 --mem=12GB  will be assigned three cores and have access to 12GB of RAM, and charged for 3 cores worth of usage (in other ways, the request --ntasks is ingored).  A job that requests  --nodes=1 --ntasks=5 --mem=12GB  will be assigned 5 cores but have access to only 12GB of RAM, and charged for 5 cores worth of usage. 

A multi-node job ( nodes>1 ) will be assigned the entire nodes with 118 GB/node and charged for the entire nodes regardless of ppn request. For example, a job that requests  --nodes=10 --ntasks-per-node=1 will be charged for 10 whole nodes (28 cores/node*10 nodes, which is 280 cores worth of usage).  

Huge Memory Node

Beginning on Tuesday, March 10th, 2020, users are able to run jobs using less than a full huge memory node. Please read the following instructions carefully before requesting a huge memory node on Owens. 

On Owens, it equates to 31,850MB/core or 1,528,800MB/node (1,492.96GB/node) for a huge memory node.

To request no more than a full huge memory node, you have two options:

  • The first is to specify the memory request between 120,832MB (118GB) and 1,528,800MB (1,492.96GB), i.e., 120832MB <= mem <=1528800MB ( 118GB <= mem < 1493GB). Note: you can only use interger for request
  • The other option is to use the combination of --ntasks-per-node and --partition, like --ntasks-per-node=4 --partition=hugemem . When no memory is specified for the huge memory node, your job is entitled to a memory allocation proportional to the number of cores requested (31,850MB/core). Note, --ntasks-per-node should be no less than 4 and no more than 48

To manage and monitor your memory usage, please refer to Out-of-Memory (OOM) or Excessive Memory Usage.

GPU Jobs

There is only one GPU per GPU node on Owens.

For serial jobs, we allow node sharing on GPU nodes so a job may request any number of cores (up to 28)

(--nodes=1 --ntasks=XX --gpus-per-node=1)

For parallel jobs (n>1), we do not allow node sharing. 

See this GPU computing page for more information. 

Partition time and job size limits

Here are the partitions available on Owens:

Name Max time limit
(dd-hh:mm:ss)
Min job size Max job size notes

serial

7-00:00:00

1 core

1 node

 

longserial

14-00:00:0

1 core

1 node

  • Restricted access (contact OSC Help if you need access)

parallel

4-00:00:00

2 nodes

81 nodes

 

gpuserial 7-00:00:00 1 core 1 node  
gpuparallel 4-00:00:00 2 nodes 8 nodes  

hugemem

7-00:00:00

1 core

1 node

 
hugemem-parallel 4-00:00:00 2 nodes 16 nodes
  • Restricted access (contact OSC Help if you need access)
debug 1:00:00 1 core 2 nodes
  • For small interactive and test jobs
gpudebug 1:00:00 1 core 2 nodes
  • For small interactive and test GPU jobs
To specify a partition for a job, either add the flag --partition=<partition-name> to the sbatch command at submission time or add this line to the job script:
#SBATCH --paritition=<partition-name>

To access one of the restricted queues, please contact OSC Help. Generally, access will only be granted to these queues if the performance of the job cannot be improved, and job size cannot be reduced by splitting or checkpointing the job.

Job/Core Limits

  Max Running Job Limit  Max Core/Processor Limit
  For all types GPU jobs Regular debug jobs GPU debug jobs For all types
Individual User 384 132 4 4 3080
Project/Group 576 132 n/a n/a 3080

An individual user can have up to the max concurrently running jobs and/or up to the max processors/cores in use.

However, among all the users in a particular group/project, they can have up to the max concurrently running jobs and/or up to the max processors/cores in use.

A user may have no more than 1000 jobs submitted to both the parallel and serial job queue separately.
Supercomputer: 
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